The invention relates generally to weather forecasting and warning systems. More particularly, the invention provides a method and apparatus for predicting precipitation over a given geographic region, watershed, or point.
Weather prediction techniques have improved greatly in recent years. As weather predictions have become more accurate, businesses have incorporated weather-related analysis into their corporate planning decisions. Information concerning tornadoes, hurricanes, severe thunderstorms and the like have been used by utility companies, manufacturing plants, airlines, and other businesses to avoid losses and to reroute vehicles such as airplanes and trucks. Government agencies, school districts, and other entities also rely on accurate weather information to decide whether to open late or take other action.
Weather-related warnings provided by the National Weather Service and other providers are broadcast over television, radio, and other communication channels to warn residents and businesses of short-term events, such as flash flooding. One problem with such warnings is that they are often provided for areas that are too broad (e.g., an entire county), thus wasting resources by unduly warning those who may not be at risk. Part of this xe2x80x9coverwarningxe2x80x9d problem is due to the current inability of meteorologists to make accurate short-term (e.g., 5 minutes to 3 hours) forecasts of precipitation over a given point, watershed, or other geographic area. Predicting future precipitation accurately to generate flash flood warnings has proven to be particularly difficult.
In the late 1940s Marshall and Palmer quantified a standard relationship between the rate of rainfall (R) and the reflected energy from a storm as measured by radar (Z), more commnonly referred to as the storm""s intensity. Intensity is measured in decibels (dB) of reflected power. The units of radar reflectivity are abbreviated as dBZ.
The well-known Marshall-Palmer relationship predicts the rate of rainfall from a given cloud formation as a function of the cloud""s radar reflectivity, and is generally given by the relationship Z=200R1.6, where Z is the reflectivity and R is the rainfall rate in millimeters per hour. More generally, the relationship is given by Z=aRb, where a and b are adjustable parameters depending on various factors. As an example, a storm returuing 50 dBZ on a WSR-88D radar will produce 2.5 inches of rain per hour, using the standard Z/R relationship for a WSR-88D radar. If this echo stays over a rain gauge for, 30 minutes, it will produce 1.25 inches of rain at the rain gauge.
It is known that the Marshall-Palmer relationship, to be usefull, should be adjusted for the air mass (e.g., a 40 dBZ echo in a summer tropical air mass will produce a much higher rate of rainfall than in a winter continental air mass); time of year; and melting level (i.e., the height of the 32 degree temperature above the ground). For example, as snowflakes fall from below freezing to above freezing temperatures, they melt from the outside in. For the few minutes where the ice crystal has a wet outer coating it is highly reflective and gives a falsely high rate of rainfall estimate when viewed in raw, uncorrected radar data. An appropriate Z/R relationship in a given situation can be selected from several published values, or it can be estimated on the fly by using one or more rain gauges to calculate the rate of rain at the gauge that is associated with the radar echoes that were passing over the gauges.
Meteorologists take advantage of this knowledge by summing the cumulative Z/R relationship over each pixel of a radar screen over varying periods of time. This produces a map of estimated precipitation for, say, one hour or three hour periods or even a xe2x80x9cstorm totalxe2x80x9d precipitation sum (i.e., the amount of precipitation since precipitation first appeared on the radar screen until the last of the precipitation ends).
However, known systems for estimating precipitation from radar are retrospective. That is, they estimate the amount of precipitation that has already fallen; they do not estimate the rain that is yet to fall.
There are generally two techniques for quantitative precipitation forecasting: Analog processes (for example, W. Smith and R. Younkin, xe2x80x9cAn Operationally Useful Relationship Between the Polar Jet Stream and Heavy Precipitation,xe2x80x9d 1972) dating back to the 1970s; and computer modeling using equations of the atmosphere that attempt to dynamically estimate precipitation amounts.
While both techniques are capable of providing moderately reliable forecasts over a broad geographic area (say, half of the State of Kansas) a day or two ahead of the event, they are virtually useless when trying to forecast the rainfall at a specific location or over a small to medium size watershed. This is because the output of both analog and dynamic models is far too smoothed to be of use in small geographic areas.
This shortcoming is especially important as hydrologists create computer models of small streams and rivers with increasing resolution and speed. Use of these models has been hampered because of the inability to input predicted rainfall information of sufficient accuracy and, in particular, to forecast predicted rainfall over the stream or river basin in question. Although the same amount of precipitation may fall over two different areas in the same county, one area may flood, whereas the other may not, depending on the hydrologic characteristics (e.g., topography, amount of paved surface, and other factors) in each area.
Hydrologists have bemoaned the fact that short-term quantitative precipitation forecasts have not been useful with mesoscale hydrologic models, which might allow more accurate forecasts for flash floods. Consequently, there has been no easy way to apply rainfall predictions to a particular area having certain hydrologic characteristics.
The invention overcomes various shortcomings described above. In one embodiment, a user-selectable cursor can be positioned over a portion of a cloud formation displayed on a computer display, where the portion corresponds to precipitation that is expected to move over a target point or area. Predicted precipitation amounts corresponding to the portion are calculated and displayed, allowing the user to see how much precipitation is expected to occur over the target point or area.
In one embodiment, radar reflectivity data is provided for a geographic area, which may be broken up into cells. A user-selectable cursor is used to select a region of interest including one or more of the cells having corresponding precipitation that is expected to move over a target area. In one variation, the cursor can be shaped to correspond to the rivershed of a given stream or other geographic shape of interest. Reflectivity values for each cell having precipitation that is expected to move over the target area are converted into predicted precipitation rates, which are multiplied by the time that precipitation from each respective cell is expected to remain over the target area. Predicted precipitation amounts attributable to each cell are accumulated and output as a predicted precipitation amount for the target area. If the target area is a watershed comprising multiple cells, then predicted precipitation amounts for each cell in the watershed are averaged to produce a predicted precipitation amount for the watershed. The cursor can be drawn over different regions of interest to allow a user to quickly identify outcomes under different scenarios.
In some embodiments, the cells having precipitation that is expected to move over a target area can be determined according to various predictive methods (e.g., a xe2x80x9cgrowth and decayxe2x80x9d algorithm), rather than using a manual cursor. The inventive principles can be applied not only to precipitation amounts estimated from ground-based radar reflectivity, but to amounts estimated from infrared satellite imagery or other estimation methods.
In some embodiments, Z/R relationships can be estimated on the fly based on air mass or by using rain gauges to calculate the amount of rain that has fallen versus the reflectivity that has already passed over each gauge. Once the Z/R relationship for a given storm has been chosen, it makes it easy to calculate how much rain has already fallen over a given point or watershed and combine that calculation with a cursor-driven or predictive model forecast of how much rain is going to fall, so that the total storm precipitation can be estimated. Once the xe2x80x9cstorm totalxe2x80x9d is known, accurate and specific warnings can be created.